Abstracting .torrent content consumption into two-mode graphs and their projection to content networks (ConNet)

Video-on-demand and live streaming services are about to take over video discs. Video streaming services typically cannot compete with the content available in Peer-to-Peer (P2P) file sharing networks. Thus, content providers can use P2P systems to identify content to include in their offer. This work defines a novel method to apply Social Network Analysis (SNA) on video streaming or download traces. Those traces are abstracted int a two-mode graph, which is projected to a content-centric one mode graph (ConNet). SNA measures are used on a ConNet to classify a content-centric graph and provide a general interpretation and insights into the system the traces were collected from. To evaluate the proposed method, real world traces acquired from BitTorrent (BT) swarms sharing movies and television (TV) shows are used to construct 48 hourly graphs to show the evolution of the graph. The results show that the video network can be classified as scale-free, that SNA measures can be used as an alternative popularity indicator, and that the network evolves over time and exhibits diurnal patterns. Finally, this work shows that the proposed method can be applied to real world traces and provides a novel perspective on video consumption.